{"id":251061,"date":"2026-09-08T15:50:34","date_gmt":"2026-09-08T10:20:34","guid":{"rendered":"https:\/\/www.oliveboard.in\/blog\/?p=251061"},"modified":"2026-09-08T16:15:52","modified_gmt":"2026-09-08T10:45:52","slug":"pfrda-grade-a-ai-ml-quiz","status":"publish","type":"post","link":"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/","title":{"rendered":"Attempt PFRDA Grade A 2026 AI\/ML Practice Quiz and Download PDF"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_77 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of content<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#Download_PFRDA_Grade_A_AIML_Practice_Quiz_PDF\" >Download PFRDA Grade A AI\/ML Practice Quiz PDF<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#Attempt_PFRDA_Grade_A_AIML_Practice_Quiz\" >Attempt PFRDA Grade A AI\/ML Practice Quiz<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#Sign_Up\" >Sign Up<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#Login\" >Login<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#Forgot_Password\" >Forgot Password<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#i\" >&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_is_the_AIML_syllabus_for_PFRDA_Grade_A_2026\" >What is the AI\/ML syllabus for PFRDA Grade A 2026?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Machine_Learning_concepts_for_PFRDA_Grade_A\" >What are the important Machine Learning concepts for PFRDA Grade A?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_types_of_Machine_Learning\" >What are the types of Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Supervised_Learning_topics\" >What are the important Supervised Learning topics?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Unsupervised_Learning_topics\" >What are the important Unsupervised Learning topics?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_should_candidates_study_under_Data_Preprocessing\" >What should candidates study under Data Preprocessing?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Model_Evaluation_concepts\" >What are the important Model Evaluation concepts?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_Overfitting_and_Underfitting_in_Machine_Learning\" >What are Overfitting and Underfitting in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_is_Cross-Validation_in_Machine_Learning\" >What is Cross-Validation in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_NLP_topics_for_PFRDA_Grade_A\" >What are the important NLP topics for PFRDA Grade A?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_is_Sentiment_Analysis\" >What is Sentiment Analysis?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_Large_Language_Models_or_LLMs\" >What are Large Language Models or LLMs?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Generative_AI_concepts\" >What are the important Generative AI concepts?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_is_Agentic_AI_and_why_is_it_important_for_PFRDA_Grade_A\" >What is Agentic AI and why is it important for PFRDA Grade A?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_is_Responsible_AI\" >What is Responsible AI?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#What_are_the_important_Reinforcement_Learning_concepts\" >What are the important Reinforcement Learning concepts?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-ai-ml-quiz\/#FAQs\" >FAQs<\/a><\/li><\/ul><\/nav><\/div>\n\n<p>The <strong>PFRDA Grade A 2026 AI\/ML Practice Quiz<\/strong> is designed to help candidates revise the newly added Artificial Intelligence and Machine Learning topics in the General Stream Paper 2 syllabus. AI\/ML is one of the major additions to the 2026 syllabus, making it important for candidates to understand both basic concepts and their practical applications. <\/p>\n\n\n\n<p>The quiz covers topics such as machine learning, data preprocessing, model evaluation, NLP, LLMs, Generative AI, Agentic AI, reinforcement learning, and Responsible AI. Regular practice can help candidates improve conceptual clarity and accuracy while preparing for the Paper 2 exam.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-download-pfrda-grade-a-ai-ml-practice-quiz-pdf\"><span class=\"ez-toc-section\" id=\"Download_PFRDA_Grade_A_AIML_Practice_Quiz_PDF\"><\/span>Download PFRDA Grade A AI\/ML Practice Quiz PDF<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The PFRDA Grade A AI\/ML Practice Quiz PDF provides practice questions based on the major AI and Machine Learning concepts included in the 2026 General Stream syllabus. Candidates can use the PDF for quick revision and practice after completing the basic concepts.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Particulars<\/strong><\/td><td><strong>Link<\/strong><\/td><\/tr><tr><td>PFRDA Grade A AI\/ML Practice Quiz<\/td><td><a href=\"https:\/\/www.oliveboard.in\/pfrda-grade-a-prep-kit\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">Download Now<\/a><\/td><\/tr><tr><td>PFRDA Grade A Stream Wise PYPs<\/td><td><a href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-assistant-manager-previous-year-paper\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">Download Now<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-attempt-pfrda-grade-a-ai-ml-practice-quiz\"><span class=\"ez-toc-section\" id=\"Attempt_PFRDA_Grade_A_AIML_Practice_Quiz\"><\/span>Attempt PFRDA Grade A AI\/ML Practice Quiz<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Attempt the free AI\/ML practice quiz to check your preparation level and identify topics that need more revision. Since AI\/ML has been newly added to the <a href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-assistant-manager-syllabus\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">PFRDA Grade A 2026 syllabus<\/a>, candidates should focus on understanding concepts instead of relying only on memorisation.<\/p>\n\n\n\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"UTF-8\">\n<link rel=\"stylesheet\" href=\"https:\/\/courses.oliveboard.in\/edge\/css\/login-modal-for-blog.css\">\n\n<style>\n#quiz-1-sticky-header {\n  position: sticky;\n  top: 0;\n  z-index: 100;\n  background: #1565c0;\n  color: #fff;\n  padding: 12px 20px;\n  border-radius: 10px 10px 0 0;\n  display: flex;\n  justify-content: space-between;\n  align-items: center;\n  font-size: 16px;\n  font-weight: bold;\n  box-shadow: 0 2px 8px rgba(0,0,0,0.2);\n  margin: -20px -20px 20px -20px;\n}\n\n#quiz-1-score-badge {\n  background: rgba(255,255,255,0.2);\n  padding: 4px 12px;\n  border-radius: 20px;\n  font-size: 15px;\n  white-space: nowrap;\n}\n#quiz-1 * { box-sizing: border-box; }\n\n#quiz-1 .quiz-container {\n  max-width: 700px;\n  margin: auto;\n  padding: 20px;\n  background: #fff;\n  border-radius: 14px;\n  box-shadow: 0 6px 16px rgba(0,0,0,0.12);\n}\n\n#quiz-1 h2 {\n  text-align: center;\n}\n\n#quiz-1 .question {\n  margin-bottom: 22px;\n}\n\n#quiz-1 .question p {\n  font-weight: bold;\n}\n\n#quiz-1 button {\n  width: 100%;\n  padding: 12px;\n  margin: 6px 0;\n  border: none;\n  border-radius: 6px;\n  background: #e0e0e0;\n  color: #000;\n  font-size: 16px;\n  cursor: pointer;\n}\n\n#quiz-1 button:hover {\n  background: #d5d5d5;\n}\n\n#quiz-1 button.correct {\n  background: #4caf50;\n  color: #000;\n}\n\n#quiz-1 button.wrong {\n  background: #f44336;\n  color: #000;\n}\n\n#quiz-1 button.locked {\n  pointer-events: none;\n}\n\n\/* SUMMARY *\/\n#quiz-1 .final-summary {\n  margin-top: 40px;\n  padding: 20px;\n  border-radius: 14px;\n  background: #fafafa;\n  border: 2px solid #4caf50;\n}\n\n#quiz-1 .final-summary h3 {\n  text-align: center;\n  margin-bottom: 20px;\n}\n\n#quiz-1 .summary-row {\n  display: grid;\n  grid-template-columns: 120px 1fr 40px;\n  gap: 10px;\n  align-items: center;\n  margin-bottom: 12px;\n  font-weight: bold;\n}\n\n#quiz-1 .summary-bar {\n  height: 14px;\n  background: #ddd;\n  border-radius: 10px;\n  overflow: hidden;\n}\n\n#quiz-1 .bar-fill {\n  height: 100%;\n  width: 0%;\n}\n\n#quiz-1 .bar-attempted { background: #2196f3; }\n#quiz-1 .bar-correct { background: #4caf50; }\n#quiz-1 .bar-wrong { background: #f44336; }\n#quiz-1 .bar-unattempted { background: #9e9e9e; width: 100%; }\n\n#quiz-1 .final-score {\n  text-align: center;\n  font-size: 22px;\n  margin-top: 20px;\n}\n\n#quiz-1 .score-value {\n  font-size: 30px;\n  margin-left: 8px;\n}\n<\/style>\n<\/head>\n\n<body>\n<div id=\"quiz-1\">\n  <div class=\"quiz-container\">\n<div id=\"quiz-1-sticky-header\">\n  <span> PFRDA Grade A AI\/ML Practice Quiz <\/span>\n  <span id=\"quiz-1-score-badge\">Score: <strong id=\"quiz-1-score-value\">0.00<\/strong><\/span>\n<\/div>\n\n    <!-- QUESTIONS -->\n\n<div class=\"question\" data-answered=\"false\">\n  <p>1. Which statement best describes Artificial Intelligence (AI)?<\/p>\n  <button data-correct=\"true\">It is the broad field of building systems that perform tasks requiring humanlike intelligence.<\/button>\n  <button data-correct=\"false\">It is only the use of spreadsheets for statistical analysis.<\/button>\n  <button data-correct=\"false\">It refers only to robots that physically resemble humans.<\/button>\n  <button data-correct=\"false\">It is a synonym for database management.<\/button>\n  <button data-correct=\"false\">It is limited to rule-based expert systems and excludes learning systems.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>2. Which relationship among AI, Machine Learning and Deep Learning is most accurate?<\/p>\n  <button data-correct=\"false\">AI is a subset of Deep Learning, which is a subset of Machine Learning.<\/button>\n  <button data-correct=\"true\">Deep Learning is a subset of Machine Learning, which is a subset of AI.<\/button>\n  <button data-correct=\"false\">Machine Learning and AI are unrelated disciplines.<\/button>\n  <button data-correct=\"false\">Deep Learning contains all of AI and all of statistics.<\/button>\n  <button data-correct=\"false\">Machine Learning is a subset of databases rather than AI.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>3. In traditional rule-based programming, how are outputs primarily produced?<\/p>\n  <button data-correct=\"false\">The system learns all rules automatically from labelled examples.<\/button>\n  <button data-correct=\"false\">The model always uses reinforcement signals instead of rules.<\/button>\n  <button data-correct=\"true\">Programmers explicitly specify rules that transform inputs into outputs.<\/button>\n  <button data-correct=\"false\">The system cannot accept numerical inputs.<\/button>\n  <button data-correct=\"false\">Outputs are generated only by large language models.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>4. In machine learning, what is a feature?<\/p>\n  <button data-correct=\"false\">The final predicted value produced by the model.<\/button>\n  <button data-correct=\"false\">A legal restriction placed on model deployment.<\/button>\n  <button data-correct=\"false\">A metric used only for regression evaluation.<\/button>\n  <button data-correct=\"true\">An input variable or measurable attribute used by a model to make a prediction.<\/button>\n  <button data-correct=\"false\">A fixed hardware component in an AI server.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>5. What is meant by inference in a machine-learning system?<\/p>\n  <button data-correct=\"false\">Deleting the training dataset after model creation.<\/button>\n  <button data-correct=\"false\">Changing labels in the training data manually.<\/button>\n  <button data-correct=\"false\">Calculating only the training loss before learning starts.<\/button>\n  <button data-correct=\"false\">Replacing all features with random values.<\/button>\n  <button data-correct=\"true\">Using a trained model to generate predictions on new data.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>6. Which of the following is primarily a regression task?<\/p>\n  <button data-correct=\"true\">Predicting the expected rupee value of a customer&#8217;s monthly expenditure.<\/button>\n  <button data-correct=\"false\">Classifying an email as spam or not spam.<\/button>\n  <button data-correct=\"false\">Assigning a transaction to one of five fraud categories.<\/button>\n  <button data-correct=\"false\">Grouping customers into segments without labels.<\/button>\n  <button data-correct=\"false\">Detecting whether a face matches a known identity.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>7. Which of the following is primarily a classification task?<\/p>\n  <button data-correct=\"false\">Estimating the exact market price of a bond tomorrow.<\/button>\n  <button data-correct=\"true\">Predicting whether a loan applicant will default or not default.<\/button>\n  <button data-correct=\"false\">Forecasting the number of transactions in the next hour.<\/button>\n  <button data-correct=\"false\">Estimating a customer&#8217;s annual income as a continuous value.<\/button>\n  <button data-correct=\"false\">Calculating the expected loss amount in rupees.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>8. What is a model parameter in machine learning?<\/p>\n  <button data-correct=\"false\">A user-selected setting such as the learning rate.<\/button>\n  <button data-correct=\"false\">A label that is manually attached to every prediction after deployment.<\/button>\n  <button data-correct=\"true\">A value learned from training data, such as a regression coefficient or neural-network weight.<\/button>\n  <button data-correct=\"false\">A data-cleaning rule that must never change.<\/button>\n  <button data-correct=\"false\">A performance metric chosen after testing.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>9. What is a hyperparameter?<\/p>\n  <button data-correct=\"false\">A model coefficient estimated directly from the training examples.<\/button>\n  <button data-correct=\"false\">The true target value in a labelled dataset.<\/button>\n  <button data-correct=\"false\">A mandatory legal disclosure for every AI model.<\/button>\n  <button data-correct=\"true\">A configuration value selected for the learning process, such as tree depth or learning rate.<\/button>\n  <button data-correct=\"false\">A test-set prediction that has been manually corrected.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>10. What does model generalization refer to?<\/p>\n  <button data-correct=\"false\">The ability to memorize every training example exactly.<\/button>\n  <button data-correct=\"false\">The ability to increase the number of features without limit.<\/button>\n  <button data-correct=\"false\">The ability to avoid using any validation data.<\/button>\n  <button data-correct=\"false\">The ability to generate only deterministic outputs.<\/button>\n  <button data-correct=\"true\">The ability of a model to perform well on new, unseen data.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>11. What is the defining characteristic of supervised learning?<\/p>\n  <button data-correct=\"true\">The training data contain input examples paired with known target labels or values.<\/button>\n  <button data-correct=\"false\">The training data contain no target information at all.<\/button>\n  <button data-correct=\"false\">The agent learns only from rewards produced by an environment.<\/button>\n  <button data-correct=\"false\">The model is never evaluated against known outcomes.<\/button>\n  <button data-correct=\"false\">The system can operate only on text data.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>12. Which algorithm is most directly associated with predicting a continuous target using a linear relationship?<\/p>\n  <button data-correct=\"false\">K-Means Clustering<\/button>\n  <button data-correct=\"true\">Linear Regression<\/button>\n  <button data-correct=\"false\">Apriori Association Mining<\/button>\n  <button data-correct=\"false\">DBSCAN<\/button>\n  <button data-correct=\"false\">Principal Component Analysis<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>13. Despite its name, Logistic Regression is commonly used for which task?<\/p>\n  <button data-correct=\"false\">Unsupervised clustering<\/button>\n  <button data-correct=\"false\">Dimensionality reduction<\/button>\n  <button data-correct=\"true\">Classification<\/button>\n  <button data-correct=\"false\">Association-rule mining<\/button>\n  <button data-correct=\"false\">Image compression only<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>14. What is a decision tree designed to do?<\/p>\n  <button data-correct=\"false\">Reduce every dataset to exactly two dimensions.<\/button>\n  <button data-correct=\"false\">Generate only random predictions to prevent bias.<\/button>\n  <button data-correct=\"false\">Store records in a relational database hierarchy.<\/button>\n  <button data-correct=\"true\">Split data through a sequence of feature-based decision rules to make predictions.<\/button>\n  <button data-correct=\"false\">Calculate the mean of the target and ignore all inputs.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>15. A Random Forest primarily improves upon a single decision tree by:<\/p>\n  <button data-correct=\"false\">Using only one deep tree with no randomness.<\/button>\n  <button data-correct=\"false\">Removing all features except the target.<\/button>\n  <button data-correct=\"false\">Replacing training data with test data.<\/button>\n  <button data-correct=\"false\">Guaranteeing zero prediction error.<\/button>\n  <button data-correct=\"true\">Combining predictions from many diverse decision trees.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>16. What is the basic idea behind K-Nearest Neighbours (KNN)?<\/p>\n  <button data-correct=\"true\">Predict using the labels or values of nearby training examples in feature space.<\/button>\n  <button data-correct=\"false\">Always fit a straight line through the entire dataset.<\/button>\n  <button data-correct=\"false\">Create random clusters without measuring similarity.<\/button>\n  <button data-correct=\"false\">Use only a single global mean for all predictions.<\/button>\n  <button data-correct=\"false\">Train a language model on token sequences.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>17. Naive Bayes classifiers rely on which simplifying assumption?<\/p>\n  <button data-correct=\"false\">All features must be perfectly correlated.<\/button>\n  <button data-correct=\"true\">Features are conditionally independent given the class.<\/button>\n  <button data-correct=\"false\">Every class must contain the same number of observations.<\/button>\n  <button data-correct=\"false\">The dataset must be unlabelled.<\/button>\n  <button data-correct=\"false\">No probability calculations are permitted.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>18. What is the key objective of a Support Vector Machine classifier?<\/p>\n  <button data-correct=\"false\">Minimize the number of rows in the dataset.<\/button>\n  <button data-correct=\"false\">Create clusters by minimizing within-cluster variance only.<\/button>\n  <button data-correct=\"true\">Find a decision boundary that maximizes the margin between classes.<\/button>\n  <button data-correct=\"false\">Predict exclusively through majority class frequency.<\/button>\n  <button data-correct=\"false\">Transform every feature into text tokens.<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>19. Which method is an ensemble technique that builds learners sequentially to correct earlier errors?<\/p>\n  <button data-correct=\"false\">K-Means<\/button>\n  <button data-correct=\"false\">PCA<\/button>\n  <button data-correct=\"false\">Ordinary least squares without iteration<\/button>\n  <button data-correct=\"true\">Gradient Boosting<\/button>\n  <button data-correct=\"false\">Simple random sampling<\/button>\n<\/div>\n\n<div class=\"question\" data-answered=\"false\">\n  <p>20. A bank has historical customer data with a known label &#8216;churned&#8217; or &#8216;not churned&#8217;. Which learning setup is most appropriate?<\/p>\n  <button data-correct=\"false\">Unsupervised clustering<\/button>\n  <button data-correct=\"false\">Reinforcement learning without labels<\/button>\n  <button data-correct=\"false\">Dimensionality reduction only<\/button>\n  <button data-correct=\"false\">Association mining only<\/button>\n  <button data-correct=\"true\">Supervised classification<\/button>\n<\/div>\n\n\n\n    <!-- FINAL SUMMARY -->\n    <div class=\"final-summary\">\n      <h3>Quiz Summary <\/h3>\n\n      <div class=\"summary-row\">\n        <span>Attempted<\/span>\n        <div class=\"summary-bar\"><div class=\"bar-fill bar-attempted\" data-summary=\"attempted\"><\/div><\/div>\n        <span data-count=\"attempted\">0<\/span>\n      <\/div>\n\n      <div class=\"summary-row\">\n        <span>Correct<\/span>\n        <div class=\"summary-bar\"><div class=\"bar-fill bar-correct\" data-summary=\"correct\"><\/div><\/div>\n        <span data-count=\"correct\">0<\/span>\n    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The topic is focused mainly on conceptual and application-oriented areas rather than advanced programming or mathematical derivations. Candidates should understand how different AI\/ML techniques work, where they are used, and how they differ from one another.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>AI\/ML Area<\/strong><\/td><td><strong>Important Topics<\/strong><\/td><\/tr><tr><td>Machine Learning<\/td><td>Supervised, unsupervised and reinforcement learning<\/td><\/tr><tr><td>Data &amp; Models<\/td><td>Data preprocessing, model training and evaluation<\/td><\/tr><tr><td>ML Concepts<\/td><td>Overfitting, underfitting, bias-variance and cross-validation<\/td><\/tr><tr><td>NLP<\/td><td>NLP, text processing and sentiment analysis<\/td><\/tr><tr><td>LLMs<\/td><td>Transformers, attention, prompts, embeddings and RAG<\/td><\/tr><tr><td>Generative AI<\/td><td>Text, image, audio, video and multimodal AI<\/td><\/tr><tr><td>Agentic AI<\/td><td>AI agents, planning, tools, memory and actions<\/td><\/tr><tr><td>Responsible AI<\/td><td>Fairness, transparency, privacy, safety and accountability<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-machine-learning-concepts-for-pfrda-grade-a\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Machine_Learning_concepts_for_PFRDA_Grade_A\"><\/span>What are the important Machine Learning concepts for PFRDA Grade A?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Machine Learning enables computer systems to learn patterns from data and use those patterns to make predictions or decisions. Candidates should understand the basic ML lifecycle, including data collection, preprocessing, model training, evaluation, deployment and monitoring. The difference between classification and regression, as well as training, validation and test data, should also be clear. Important Machine Learning concepts include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Meaning and applications of Machine Learning<\/li>\n\n\n\n<li>Machine Learning lifecycle<\/li>\n\n\n\n<li>Features and target variables<\/li>\n\n\n\n<li>Training, validation and test data<\/li>\n\n\n\n<li>Parameters and hyperparameters<\/li>\n\n\n\n<li>Classification and regression<\/li>\n\n\n\n<li>Training and inference<\/li>\n\n\n\n<li>Generalisation of ML models<\/li>\n\n\n\n<li>Model deployment and monitoring<\/li>\n<\/ul>\n\n\n\n<p><strong>Also Check: <a href=\"https:\/\/www.oliveboard.in\/blog\/how-to-prepare-ai-ml-for-pfrda-grade-a\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">How to prepare AI\/ML for PFRDA Grade A<\/a><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-types-of-machine-learning\"><span class=\"ez-toc-section\" id=\"What_are_the_types_of_Machine_Learning\"><\/span>What are the types of Machine Learning?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Machine Learning can broadly be divided into <strong>supervised learning, unsupervised learning and reinforcement learning<\/strong>. The main difference is the type of data or feedback used during the learning process. Candidates should understand the purpose, basic working and common applications of each type.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Type<\/strong><\/td><td><strong>Key Idea<\/strong><\/td><td><strong>Example<\/strong><\/td><\/tr><tr><td>Supervised Learning<\/td><td>Learns from labelled data<\/td><td>Fraud detection<\/td><\/tr><tr><td>Unsupervised Learning<\/td><td>Finds patterns in unlabelled data<\/td><td>Customer segmentation<\/td><\/tr><tr><td>Reinforcement Learning<\/td><td>Learns using rewards and penalties<\/td><td>Robot navigation<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-supervised-learning-topics\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Supervised_Learning_topics\"><\/span>What are the important Supervised Learning topics?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Supervised Learning uses labelled datasets where the expected output is already known. The model learns the relationship between input variables and known outcomes and then uses this learning to make predictions on new data. Candidates should understand the difference between classification and regression and the basic purpose of commonly used algorithms. Important areas include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Classification<\/li>\n\n\n\n<li>Binary classification<\/li>\n\n\n\n<li>Multiclass classification<\/li>\n\n\n\n<li>Regression<\/li>\n\n\n\n<li>Linear Regression<\/li>\n\n\n\n<li>Logistic Regression<\/li>\n\n\n\n<li>Decision Tree<\/li>\n\n\n\n<li>Random Forest<\/li>\n\n\n\n<li>Support Vector Machine<\/li>\n\n\n\n<li>K-Nearest Neighbours<\/li>\n\n\n\n<li>Naive Bayes<\/li>\n\n\n\n<li>Gradient Boosting<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-unsupervised-learning-topics\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Unsupervised_Learning_topics\"><\/span>What are the important Unsupervised Learning topics?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Unsupervised Learning works with data that does not have predefined labels. It is mainly used to identify hidden patterns, groups or relationships within a dataset. Candidates should focus on clustering, dimensionality reduction and association analysis and understand their practical applications. Important topics include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Unlabelled datasets<\/li>\n\n\n\n<li>Clustering<\/li>\n\n\n\n<li>K-Means Clustering<\/li>\n\n\n\n<li>Hierarchical Clustering<\/li>\n\n\n\n<li>Principal Component Analysis (PCA)<\/li>\n\n\n\n<li>Dimensionality reduction<\/li>\n\n\n\n<li>Association analysis<\/li>\n\n\n\n<li>Market basket analysis<\/li>\n\n\n\n<li>Customer segmentation<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-should-candidates-study-under-data-preprocessing\"><span class=\"ez-toc-section\" id=\"What_should_candidates_study_under_Data_Preprocessing\"><\/span>What should candidates study under Data Preprocessing?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Data preprocessing prepares raw data before it is given to a Machine Learning model. Poor-quality data can affect model performance, which is why concepts such as missing values, duplicate records, outliers and data transformation are important. Candidates should also understand feature selection, encoding, scaling and the handling of imbalanced datasets. Key areas for preparation are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Data cleaning<\/li>\n\n\n\n<li>Missing values<\/li>\n\n\n\n<li>Duplicate records<\/li>\n\n\n\n<li>Outliers<\/li>\n\n\n\n<li>Categorical data<\/li>\n\n\n\n<li>Encoding<\/li>\n\n\n\n<li>Normalisation<\/li>\n\n\n\n<li>Standardisation<\/li>\n\n\n\n<li>Feature selection<\/li>\n\n\n\n<li>Feature extraction<\/li>\n\n\n\n<li>Data transformation<\/li>\n\n\n\n<li>Imbalanced datasets<\/li>\n\n\n\n<li>Train-test split<\/li>\n\n\n\n<li>Garbage In, Garbage Out (GIGO)<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.oliveboard.in\/pfrda-grade-a-exam\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Explore PFRDA Grade A Online Coaching Classes<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-model-evaluation-concepts\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Model_Evaluation_concepts\"><\/span>What are the important Model Evaluation concepts?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Model evaluation helps determine how well a Machine Learning model performs on data. Candidates should understand common classification metrics such as accuracy, precision, recall, specificity and F1 Score. For regression, basic knowledge of MAE, MSE, RMSE and R-squared is important.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Area<\/strong><\/td><td><strong>Important Concepts<\/strong><\/td><\/tr><tr><td>Classification<\/td><td>Accuracy, Precision, Recall, Specificity, F1 Score<\/td><\/tr><tr><td>Confusion Matrix<\/td><td>TP, TN, FP, FN<\/td><\/tr><tr><td>Curve-based Metrics<\/td><td>ROC Curve, AUC<\/td><\/tr><tr><td>Regression<\/td><td>MAE, MSE, RMSE, R-squared<\/td><\/tr><tr><td>Model Performance<\/td><td>Training vs testing performance<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-overfitting-and-underfitting-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_are_Overfitting_and_Underfitting_in_Machine_Learning\"><\/span>What are Overfitting and Underfitting in Machine Learning?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Overfitting occurs when a model learns the training data too specifically and performs poorly on unseen data. Underfitting occurs when the model is too simple to identify important patterns in the data. Candidates should understand their causes, signs and methods used to improve model performance.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Concept<\/strong><\/td><td><strong>Meaning<\/strong><\/td><td><strong>Common Solution<\/strong><\/td><\/tr><tr><td>Overfitting<\/td><td>Model performs well on training data but poorly on new data<\/td><td>Cross-validation, regularisation, more data<\/td><\/tr><tr><td>Underfitting<\/td><td>Model performs poorly on both training and testing data<\/td><td>Increase complexity, add useful features<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Candidates should also revise the <strong>bias-variance trade-off<\/strong>, where high bias is generally linked with underfitting and high variance with overfitting.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-cross-validation-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_Cross-Validation_in_Machine_Learning\"><\/span>What is Cross-Validation in Machine Learning?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Cross-validation is a technique used to check how well a model is likely to perform on unseen data. In K-Fold Cross-Validation, the dataset is divided into K parts and the model is trained and evaluated multiple times using different portions of the data. Candidates should understand the purpose and basic working of K-Fold, Stratified K-Fold and Leave-One-Out Cross-Validation. Important concepts include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Meaning of cross-validation<\/li>\n\n\n\n<li>K-Fold Cross-Validation<\/li>\n\n\n\n<li>Stratified K-Fold<\/li>\n\n\n\n<li>Leave-One-Out Cross-Validation<\/li>\n\n\n\n<li>Training-validation-test concept<\/li>\n\n\n\n<li>Advantages and limitations of cross-validation<\/li>\n<\/ul>\n\n\n\n<p><strong>Check: <a href=\"https:\/\/www.oliveboard.in\/blog\/major-changes-in-pfrda-grade-a-2026\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">What are the major changes in the PFRDA Grade A 2026<\/a><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-nlp-topics-for-pfrda-grade-a\"><span class=\"ez-toc-section\" id=\"What_are_the_important_NLP_topics_for_PFRDA_Grade_A\"><\/span>What are the important NLP topics for PFRDA Grade A?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Natural Language Processing (NLP) is a branch of AI that enables computers to process and understand human language. Candidates should understand basic text-processing techniques and common NLP applications. The syllabus should also be studied in the context of modern AI systems such as chatbots and language models. Important NLP topics include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Meaning of NLP<\/li>\n\n\n\n<li>NLP pipeline<\/li>\n\n\n\n<li>Tokenization<\/li>\n\n\n\n<li>Stop-word removal<\/li>\n\n\n\n<li>Stemming<\/li>\n\n\n\n<li>Lemmatization<\/li>\n\n\n\n<li>Part-of-Speech tagging<\/li>\n\n\n\n<li>Named Entity Recognition<\/li>\n\n\n\n<li>Text classification<\/li>\n\n\n\n<li>Word embeddings<\/li>\n\n\n\n<li>Sentiment analysis<\/li>\n\n\n\n<li>Chatbots<\/li>\n\n\n\n<li>Machine translation<\/li>\n\n\n\n<li>Text summarisation<\/li>\n\n\n\n<li>Question answering<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.oliveboard.in\/esi-and-finance-mcqs-for-pfrda-grade-a\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">Check 200+ ESI &amp; Finance MCQs for PFRDA Grade A<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-sentiment-analysis\"><span class=\"ez-toc-section\" id=\"What_is_Sentiment_Analysis\"><\/span>What is Sentiment Analysis?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Sentiment Analysis is an NLP application used to identify the opinion or attitude expressed in text. It commonly classifies text as positive, negative or neutral. Candidates should also understand advanced concepts such as emotion detection and aspect-based sentiment analysis, along with limitations caused by sarcasm, context and multilingual language. Common applications include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Customer feedback analysis<\/li>\n\n\n\n<li>Product-review analysis<\/li>\n\n\n\n<li>Social-media monitoring<\/li>\n\n\n\n<li>Brand monitoring<\/li>\n\n\n\n<li>Market research<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-large-language-models-or-llms\"><span class=\"ez-toc-section\" id=\"What_are_Large_Language_Models_or_LLMs\"><\/span>What are Large Language Models or LLMs?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Large Language Models are AI models trained on large amounts of text data to understand and generate human-like language. Candidates should understand basic concepts such as tokens, parameters, context windows, prompts, training and inference. They should also be familiar with the Transformer architecture, attention, self-attention, embeddings, fine-tuning and Retrieval-Augmented Generation (RAG).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>LLM Concept<\/strong><\/td><td><strong>Basic Meaning<\/strong><\/td><\/tr><tr><td>Token<\/td><td>A unit of text processed by the model<\/td><\/tr><tr><td>Parameter<\/td><td>Learned value used by the model<\/td><\/tr><tr><td>Context Window<\/td><td>Amount of information the model can consider<\/td><\/tr><tr><td>Prompt<\/td><td>Input or instruction given to the model<\/td><\/tr><tr><td>Embedding<\/td><td>Numerical representation of information<\/td><\/tr><tr><td>Fine-Tuning<\/td><td>Further training for a specific task<\/td><\/tr><tr><td>RAG<\/td><td>Uses external information to improve generated responses<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-generative-ai-concepts\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Generative_AI_concepts\"><\/span>What are the important Generative AI concepts?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Generative AI refers to AI systems that can create new content such as text, images, audio, video and code. Candidates should understand the difference between generative and discriminative AI and have basic awareness of foundation models and multimodal AI. The risks associated with AI-generated content should also be studied. Important topics include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Generative AI<\/li>\n\n\n\n<li>Generative vs discriminative AI<\/li>\n\n\n\n<li>Foundation models<\/li>\n\n\n\n<li>Text generation<\/li>\n\n\n\n<li>Image generation<\/li>\n\n\n\n<li>Audio generation<\/li>\n\n\n\n<li>Video generation<\/li>\n\n\n\n<li>Code generation<\/li>\n\n\n\n<li>Multimodal AI<\/li>\n\n\n\n<li>Prompt engineering<\/li>\n\n\n\n<li>Synthetic data<\/li>\n\n\n\n<li>AI-generated content<\/li>\n\n\n\n<li>Deepfakes and misinformation<\/li>\n\n\n\n<li>Copyright concerns<\/li>\n\n\n\n<li>Hallucinations<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-agentic-ai-and-why-is-it-important-for-pfrda-grade-a\"><span class=\"ez-toc-section\" id=\"What_is_Agentic_AI_and_why_is_it_important_for_PFRDA_Grade_A\"><\/span>What is Agentic AI and why is it important for PFRDA Grade A?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Agentic AI refers to AI systems that can plan tasks, make decisions and take actions to achieve a particular goal. Unlike a system that only responds to a single prompt, an AI agent can work through multiple steps and use tools or external systems. Candidates should understand the basic agent workflow and the risks associated with autonomous actions. Important areas include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AI agents<\/li>\n\n\n\n<li>Autonomous and semi-autonomous agents<\/li>\n\n\n\n<li>Goals<\/li>\n\n\n\n<li>Perception and input<\/li>\n\n\n\n<li>Reasoning<\/li>\n\n\n\n<li>Planning<\/li>\n\n\n\n<li>Memory<\/li>\n\n\n\n<li>Tools<\/li>\n\n\n\n<li>Actions<\/li>\n\n\n\n<li>Feedback<\/li>\n\n\n\n<li>Tool calling<\/li>\n\n\n\n<li>Task decomposition<\/li>\n\n\n\n<li>Workflow automation<\/li>\n\n\n\n<li>Single-agent systems<\/li>\n\n\n\n<li>Multi-agent systems<\/li>\n\n\n\n<li>Human-in-the-loop systems<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-practice-quiz\/?ref=contac-pfrda-ai-ml-pq\" target=\"_blank\" rel=\"noreferrer noopener\">Attempt PFRDA Grade A Practice Quiz<\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-responsible-ai\"><span class=\"ez-toc-section\" id=\"What_is_Responsible_AI\"><\/span>What is Responsible AI?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Responsible AI focuses on developing and using AI systems in a safe, fair, transparent and accountable manner. This is especially relevant for regulated sectors where automated decisions can affect customers and organisations. Candidates should understand AI bias, explainability, privacy, security and the role of human oversight.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Responsible AI Area<\/strong><\/td><td><strong>What to Understand<\/strong><\/td><\/tr><tr><td>Fairness<\/td><td>Avoiding unfair or discriminatory outcomes<\/td><\/tr><tr><td>Accountability<\/td><td>Assigning responsibility for AI decisions<\/td><\/tr><tr><td>Transparency<\/td><td>Making AI systems and decisions understandable<\/td><\/tr><tr><td>Explainability<\/td><td>Understanding why a model produced an output<\/td><\/tr><tr><td>Privacy<\/td><td>Protecting personal and sensitive data<\/td><\/tr><tr><td>Security<\/td><td>Protecting AI systems from attacks and misuse<\/td><\/tr><tr><td>Human Oversight<\/td><td>Keeping appropriate human control over AI decisions<\/td><\/tr><tr><td>Reliability &amp; Safety<\/td><td>Ensuring dependable and safe AI outputs<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-are-the-important-reinforcement-learning-concepts\"><span class=\"ez-toc-section\" id=\"What_are_the_important_Reinforcement_Learning_concepts\"><\/span>What are the important Reinforcement Learning concepts?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Reinforcement Learning allows an agent to learn through interaction with an environment. Instead of using labelled data, the agent receives rewards or penalties based on its actions and learns to improve its decisions. Candidates should understand the basic framework and terms such as state, action, reward, policy and value. Important topics include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Agent<\/li>\n\n\n\n<li>Environment<\/li>\n\n\n\n<li>State<\/li>\n\n\n\n<li>Action<\/li>\n\n\n\n<li>Reward<\/li>\n\n\n\n<li>Policy<\/li>\n\n\n\n<li>Value<\/li>\n\n\n\n<li>Episode<\/li>\n\n\n\n<li>Exploration vs exploitation<\/li>\n\n\n\n<li>Q-Learning<\/li>\n\n\n\n<li>Deep Q-Networks<\/li>\n\n\n\n<li>Policy-based approaches<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-faqs\"><span class=\"ez-toc-section\" id=\"FAQs\"><\/span>FAQs<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<div class=\"schema-faq wp-block-yoast-faq-block\"><div class=\"schema-faq-section\" id=\"faq-question-1788857729710\"><strong class=\"schema-faq-question\">1. Is AI\/ML included in the PFRDA Grade A 2026 syllabus?<\/strong> <p class=\"schema-faq-answer\">Yes, AI\/ML is one of the new topics added to the General Stream Paper 2 syllabus in 2026.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1788857730665\"><strong class=\"schema-faq-question\">2. What AI\/ML topics should candidates study for PFRDA Grade A?<\/strong> <p class=\"schema-faq-answer\">Candidates should cover ML, NLP, LLMs, Generative AI, Agentic AI, Responsible AI and related concepts.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1788857731361\"><strong class=\"schema-faq-question\">3. Is coding required for PFRDA Grade A AI\/ML preparation?<\/strong> <p class=\"schema-faq-answer\">The AI\/ML syllabus is primarily conceptual and application-oriented, so advanced coding is generally not the focus.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1788857731960\"><strong class=\"schema-faq-question\">4. What are the major Machine Learning types?<\/strong> <p class=\"schema-faq-answer\">The major types are supervised learning, unsupervised learning and reinforcement learning.<\/p> <\/div> <div class=\"schema-faq-section\" id=\"faq-question-1788857732576\"><strong class=\"schema-faq-question\">5. What should candidates study under Responsible AI?<\/strong> <p class=\"schema-faq-answer\">Candidates should revise fairness, accountability, transparency, explainability, privacy, security, safety and human oversight.<\/p> <\/div> <\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n<ul class=\"wp-block-latest-posts__list is-grid columns-3 wp-block-latest-posts\"><li><a class=\"wp-block-latest-posts__post-title\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-assistant-manager-eligibility\/\">PFRDA Grade A Eligibility 2026, Age Limit and Qualification<\/a><\/li>\n<li><a class=\"wp-block-latest-posts__post-title\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-grade-a-apply-online\/\">PFRDA Grade A Apply Online 2026, Application Form Link Active<\/a><\/li>\n<li><a class=\"wp-block-latest-posts__post-title\" href=\"https:\/\/www.oliveboard.in\/blog\/pfrda-assistant-manager-notification\/\">PFRDA Grade A Recruitment Notice 2026 Out, Download Notification 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and<\/p>\n","protected":false},"author":58,"featured_media":251062,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11047,10247],"tags":[],"class_list":["post-251061","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pfrda-grade-a","category-rbi-sebi-nabard","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.6 (Yoast SEO v26.6) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Attempt PFRDA Grade A 2026 AI\/ML Practice Quiz and Download PDF<\/title>\n<meta name=\"description\" content=\"Attempt the AI and ML Practice Questions and Level Up Your Paper 2 Exam Preparation for the Upcoming Exam Scheduled for 15th October 2026.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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What should candidates study under Responsible AI?","answerCount":1,"acceptedAnswer":{"@type":"Answer","text":"Candidates should revise fairness, accountability, transparency, explainability, privacy, security, safety and human oversight.","inLanguage":"en-US"},"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/posts\/251061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/comments?post=251061"}],"version-history":[{"count":3,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/posts\/251061\/revisions"}],"predecessor-version":[{"id":251074,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/posts\/251061\/revisions\/251074"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/media\/251062"}],"wp:attachment":[{"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/media?parent=251061"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/categories?post=251061"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.oliveboard.in\/blog\/wp-json\/wp\/v2\/tags?post=251061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}